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Large Language Models (LLMs) have significantly advanced the field of information retrieval, particularly for reranking.
Rodrigo Nogueira and Kyunghyun Cho. 2019 · 1901
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Relevance feedback information retrieval
J ROCCHIO. 1971 · 1971
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Pranking with ranking
Koby Crammer and Yoram Singer. 2001 · 2001
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Learning to rank using gradient descent
Chris Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, and Greg Hullender. 2005 · 2005
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Learning to rank with nonsmooth cost functions
Christopher Burges, Robert Ragno, and Quoc Le. 2006 · 2006
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Learning to rank: from pairwise approach to listwise approach
Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, and Hang Li. 2007 · 2007
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Mcrank: Learning to rank using multiple classification and gradient boosting
Ping Li, Qiang Wu, and Christopher Burges. 2007 · 2007
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Softrank: optimizing non-smooth rank metrics
Michael Taylor, John Guiver, Stephen Robertson, and Tom Minka. 2008 · 2008
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Listwise approach to learning to rank: theory and algorithm
Fen Xia, Tie-Yan Liu, Jue Wang, Wensheng Zhang, and Hang Li. 2008 · 2008
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Learning to rank for information retrieval
Tie-Yan Liu et al. 2009 · 2009
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The probabilistic relevance framework: Bm25 and beyond
Stephen Robertson, Hugo Zaragoza, et al. 2009 · 2009
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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
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Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020 · 2020
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Document ranking with a pretrained sequence-to-sequence model
Rodrigo Nogueira, Zhiying Jiang, Ronak Pradeep, and Jimmy Lin. 2020 · 2020
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Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters
Jeff Rasley, Samyam Rajbhandari, Olatunji Ruwase, and Yuxiong He. 2020 · 2020
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Augmented SBERT: Data augmentation method for improving bi-encoders for pairwise sentence scoring tasks
Nandan Thakur, Nils Reimers, Johannes Daxenberger, and Iryna Gurevych. 2021a · 2021
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Unsupervised dense information retrieval with contrastive learning
Izacard Gautier, Caron Mathilde, Hosseini Lucas, Riedel Sebastian, Bojanowski Piotr, Joulin Armand, and Grave Edouard. 2022 · 2022
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Improving passage retrieval with zero-shot question generation
Devendra Sachan, Mike Lewis, Mandar Joshi, Armen Aghajanyan, Wen-tau Yih, Joelle Pineau, and Luke Zettlemoyer. 2022 · 2022
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Large language models are effective text rankers with pairwise ranking prompting
Zhen Qin, Rolf Jagerman, Kai Hui, Honglei Zhuang, Junru Wu, Jiaming Shen, Tianqi Liu, Jialu Liu, Donald Metzler, Xuanhui Wang, et al. 2023 · 2023
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Inference-time re-ranker relevance feedback for neural information retrieval
Revanth Gangi Reddy, Pradeep Dasigi, Md Arafat Sultan, Arman Cohan, Avirup Sil, Heng Ji, and Hannaneh Hajishirzi. 2023 · 2023
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Is chatgpt good at search? investigating large language models as re-ranking agents
Weiwei Sun, Lingyong Yan, Xinyu Ma, Shuaiqiang Wang, Pengjie Ren, Zhumin Chen, Dawei Yin, and Zhaochun Ren. 2023 · 2023
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Optimizing test-time query representations for dense retrieval
Mujeen Sung, Jungsoo Park, Jaewoo Kang, Danqi Chen, and Jinhyuk Lee. 2023 · 2023
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Found in the middle: Permutation self-consistency improves listwise ranking in large language models
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Emergent abilities of large language models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al. 2022 · 2022
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023 · 2023
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Discrete prompt optimization via constrained generation for zero-shot re-ranker
Sukmin Cho, Soyeong Jeong, Jeong yeon Seo, and Jong C Park. 2023 · 2023
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Ultrafeedback: Boosting language models with high-quality feedback
Ganqu Cui, Lifan Yuan, Ning Ding, Guanming Yao, Wei Zhu, Yuan Ni, Guotong Xie, Zhiyuan Liu, and Maosong Sun. 2023 · 2023
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Enhancing chat language models by scaling high-quality instructional conversations
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Neel Jain, Ping-yeh Chiang, Yuxin Wen, John Kirchenbauer, Hong-Min Chu, Gowthami Somepalli, Brian R Bartoldson, Bhavya Kailkhura, Avi Schwarzschild, Aniruddha Saha, et al. 2023 · 2023
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Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. 2023 · 2023
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Raphael Tang, Xinyu Zhang, Xueguang Ma, Jimmy Lin, and Ferhan Ture. 2023 · 2023
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Zephyr: Direct distillation of lm alignment
Lewis Tunstall, Edward Beeching, Nathan Lambert, Nazneen Rajani, Kashif Rasul, Younes Belkada, Shengyi Huang, Leandro von Werra, Clémentine Fourrier, Nathan Habib, Nathan Sarrazin, Omar Sanseviero, Alexander M. Rush, and Thomas Wolf. 2023 · 2023
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Learning list-level domain-invariant representations for ranking
Ruicheng Xian, Honglei Zhuang, Zhen Qin, Hamed Zamani, Jing Lu, Ji Ma, Kai Hui, Han Zhao, Xuanhui Wang, and Michael Bendersky. 2023 · 2023
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Large language models for information retrieval: A survey
Yutao Zhu, Huaying Yuan, Shuting Wang, Jiongnan Liu, Wenhan Liu, Chenlong Deng, Zhicheng Dou, and Ji-Rong Wen. 2023 · 2023
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Open-source large language models are strong zero-shot query likelihood models for document ranking
Shengyao Zhuang, Bing Liu, Bevan Koopman, and Guido Zuccon. 2023c · 2023
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Ranked list truncation for large language model-based re-ranking
Chuan Meng, Negar Arabzadeh, Arian Askari, Mohammad Aliannejadi, and Maarten de Rijke. 2024 · 2024
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Top-down partitioning for efficient list-wise ranking
Andrew Parry, Sean MacAvaney, and Debasis Ganguly. 2024 · 2024
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Large search model: Redefining search stack in the era of llms
Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, and Furu Wei. 2024 · 2024
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